Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

Xu, Suan; Zeyu, Wu; Wang, Jing; Hong, Kaixing; Hu, Kai-Ming

doi:10.1177/09596518211064667

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The hysteresis phenomenon can also be found in various fields, including physics, chemistry, engineering, biology and economics. Moreover, previous models, such as the Preisach (Roussel et al, 2022a,b) and Duhem models (Ahmed et al, 2021;Xu et al, 2022) have been proposed to describe nonlinear hysteresis systems. Many dynamic models have also been developed to describe such hysteresis behavior, and it is necessary to select and implement high-accuracy models to capture nonlinear hysteretic responses.…”

Section: Mathematical Model Based On the Lugre Modelmentioning

confidence: 99%

A study of dynamic hysteresis model for a magnetorheological damper

Chen¹,

Kung²

2023

Journal of Theoretical and Applied Mechanics

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The paper proposes a new dynamic model based on the LuGre model and an electrical equation to describe the hysteresis phenomenon for a magnetorheological (MR) damper. In addition, a sliding mode observer (SMO) is proposed to estimate unmeasurable states of the MR damper. The parameters of the MR damper are successfully identified by using the self-learning particle swarm optimization (SLPSO) algorithm. The contributions of this paper are: i) a new dynamic model based on the LuGre model and an electrical equation for an MR damper is successfully formulated to fit for the hysteresis behavior, ii) the exerted damping force can be practically adjusted by using input voltage for the dynamic model, iii) the SMO is proposed to estimate the internal states and current, and iv) the unknown parameters of the MR damper are successfully identified by using the SLPSO algorithm with a numerical experiment.

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Section: Mathematical Model Based On the Lugre Modelmentioning

confidence: 99%

A study of dynamic hysteresis model for a magnetorheological damper

Chen¹,

Kung²

2023

Journal of Theoretical and Applied Mechanics

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“…The model of PEA can be divided into two parts: a hysteresis model H ( t ) and a transfer function G ( z ) . 30 When the signal is input to the inverse model in series with the PEA, the output can be expressed as follows:…”

Section: Feedforward and Robust Backstepping Sliding Mode Feedback Co...mentioning

confidence: 99%

Parameter identification of Duhem model based on antlion-fish swarm hybrid algorithm and robust backstepping sliding mode control for rate-dependent hysteresis of piezoelectric actuators

Zeyu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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A new parameter identification method of Duhem model based on antlion-fish swarm hybrid algorithm is proposed to characterize the rate-dependent hysteresis. The unknown piecewise functions in Duhem model is identified by a hybrid algorithm. Antlion optimization (ALO) algorithm and artificial fish (AF) swarm algorithm are switched in order of execution and their iterative data are fused to form the antlion-fish swarm hybrid algorithm. The comparison experiment shows the hybrid algorithm has the advantages of fast convergence and high precision. In order to compensate the rate-dependent hysteresis of PEA, a robust backstepping sliding mode feedback controller with feedforward inverse Duhem model compensator are designed. The comparative experimental results of displacement tracking show that this method has good tracking performance. The practicability and effectiveness of the method are verified.

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A precision-drive hysteresis model with an equal-density weight function for GMA feedforward compensation

Kun

Wang

et al. 2023

Nanotechnology and Precision Engineering

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Giant magnetostrictive actuators (GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations, including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function (E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 µm and decreases the root-mean-square error in displacement to within 0.5 µm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data; it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.

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Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

Cited by 5 publications

References 26 publications

A study of dynamic hysteresis model for a magnetorheological damper

A study of dynamic hysteresis model for a magnetorheological damper

Parameter identification of Duhem model based on antlion-fish swarm hybrid algorithm and robust backstepping sliding mode control for rate-dependent hysteresis of piezoelectric actuators

A precision-drive hysteresis model with an equal-density weight function for GMA feedforward compensation

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